This notebook contains a set of analyses for analyzing ZeeGarcia’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
ZeeGarcia | training | published before 2020 | 1,588 | 2,057 |
ZeeGarcia | validation | published 2020 | 57 | 85 |
ZeeGarcia | test | published after 2020 | 58 | 65 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
ZeeGarcia | ZMan Games | 6.2% | 1.1% | 5.86 |
ZeeGarcia | Rio Grande Games | 7.1% | 1.5% | 4.78 |
ZeeGarcia | Asmodee | 9.4% | 2.1% | 4.49 |
ZeeGarcia | Fantasy Flight Games | 4.2% | 0.9% | 4.45 |
ZeeGarcia | Tricktaking | 5.0% | 1.5% | 3.39 |
ZeeGarcia | Games With Solitaire Rules | 9.1% | 4.9% | 1.85 |
ZeeGarcia | Card Game | 46.6% | 28.1% | 1.66 |
ZeeGarcia | Parker Brothers | 2.3% | 2.5% | 0.95 |
ZeeGarcia | Miniatures Game | 2.5% | 5.0% | 0.49 |
ZeeGarcia | Movies TV Radio Theme | 2.3% | 5.2% | 0.43 |
ZeeGarcia | Self-Published | 1.0% | 2.8% | 0.36 |
ZeeGarcia | Action Dexterity | 1.5% | 5.5% | 0.27 |
ZeeGarcia | Childrens Game | 1.7% | 8.5% | 0.20 |
ZeeGarcia | Wargame | 1.4% | 20.2% | 0.07 |
ZeeGarcia | Movement Points | 0.2% | 2.6% | 0.07 |
ZeeGarcia | Simulation | 0.7% | 11.0% | 0.06 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2009 | 54998 | Cyclades | 0.995 | yes |
2 | 2015 | 176920 | Mission: Red Planet (Second Edition) | 0.995 | yes |
3 | 2003 | 6068 | Queen's Necklace | 0.994 | yes |
4 | 2005 | 15062 | Shadows over Camelot | 0.993 | yes |
5 | 2007 | 29030 | Chicago Poker | 0.990 | yes |
6 | 2015 | 173346 | 7 Wonders Duel | 0.989 | yes |
7 | 2014 | 157354 | Five Tribes | 0.988 | yes |
8 | 2015 | 177639 | Raptor | 0.988 | yes |
9 | 2006 | 22141 | Cleopatra and the Society of Architects | 0.987 | yes |
10 | 2000 | 478 | Citadels | 0.986 | yes |
11 | 2014 | 155987 | Abyss | 0.984 | yes |
12 | 2005 | 18258 | Mission: Red Planet | 0.983 | yes |
13 | 2018 | 239840 | Micropolis | 0.982 | yes |
14 | 2008 | 33107 | Senji | 0.981 | yes |
15 | 2016 | 205398 | Citadels | 0.980 | yes |
16 | 2016 | 200147 | Kanagawa | 0.978 | yes |
17 | 2004 | 10997 | Boomtown | 0.970 | yes |
18 | 2013 | 134453 | The Little Prince: Make Me a Planet | 0.968 | yes |
19 | 2019 | 265285 | Queenz: To Bee or Not to Bee | 0.964 | yes |
20 | 2016 | 205610 | A Game of Thrones: Hand of the King | 0.963 | yes |
21 | 2009 | 40793 | Dice Town | 0.959 | yes |
22 | 2012 | 116858 | Noah | 0.957 | yes |
23 | 2011 | 70919 | Takenoko | 0.955 | yes |
24 | 2012 | 129904 | Shadows over Camelot: The Card Game | 0.954 | no |
25 | 2017 | 213893 | Yamataï | 0.952 | yes |
26 | 2019 | 286096 | Tapestry | 0.951 | no |
27 | 2001 | 878 | Wyatt Earp | 0.946 | yes |
28 | 2014 | 154443 | Madame Ching | 0.945 | yes |
29 | 1997 | 42 | Tigris & Euphrates | 0.943 | yes |
30 | 2017 | 197178 | DIG | 0.942 | yes |
31 | 2014 | 148228 | Splendor | 0.940 | yes |
32 | 2014 | 165662 | Haru Ichiban | 0.937 | yes |
33 | 2019 | 281960 | Kingdomino Duel | 0.932 | yes |
34 | 2011 | 103686 | Mundus Novus | 0.931 | yes |
35 | 2016 | 193210 | Dice Stars | 0.930 | yes |
36 | 2006 | 24845 | Tomahawk | 0.928 | yes |
37 | 2002 | 4471 | Fist of Dragonstones | 0.925 | yes |
38 | 2017 | 221107 | Pandemic Legacy: Season 2 | 0.924 | no |
39 | 2018 | 259829 | Loser | 0.924 | no |
40 | 2000 | 823 | The Lord of the Rings | 0.921 | yes |
41 | 2016 | 182120 | Histrio | 0.914 | yes |
42 | 2016 | 190639 | Zany Penguins | 0.913 | yes |
43 | 2019 | 251830 | Alhambra: Mega Box | 0.911 | yes |
44 | 2014 | 150926 | Roll Through the Ages: The Iron Age | 0.909 | yes |
45 | 2006 | 21763 | Mr. Jack | 0.908 | yes |
46 | 2004 | 12942 | No Thanks! | 0.905 | yes |
47 | 2005 | 18588 | Les Fils de Samarande | 0.904 | no |
48 | 2019 | 265736 | Tiny Towns | 0.901 | no |
49 | 2018 | 199792 | Everdell | 0.898 | yes |
50 | 2016 | 201920 | Pocket Madness | 0.897 | yes |
51 | 2018 | 244330 | Scarabya | 0.895 | yes |
52 | 2010 | 67185 | Sobek | 0.894 | yes |
53 | 2014 | 154600 | Desperados of Dice Town | 0.893 | yes |
54 | 2012 | 125311 | Okiya | 0.891 | yes |
55 | 2014 | 132531 | Roll for the Galaxy | 0.891 | yes |
56 | 2013 | 143157 | SOS Titanic | 0.889 | yes |
57 | 2009 | 40237 | Long Shot | 0.885 | yes |
58 | 2019 | 285984 | Last Bastion | 0.882 | yes |
59 | 2017 | 192827 | RUM | 0.882 | yes |
60 | 2004 | 14781 | Drôles de Zèbres | 0.881 | yes |
61 | 2019 | 265031 | Ice Team | 0.878 | no |
62 | 2019 | 270971 | Era: Medieval Age | 0.878 | no |
63 | 2019 | 244191 | Naga Raja | 0.869 | yes |
64 | 2019 | 276042 | Conspiracy: Abyss Universe | 0.867 | yes |
65 | 2011 | 100423 | Elder Sign | 0.867 | yes |
66 | 1998 | 503 | Through the Desert | 0.865 | yes |
67 | 2004 | 9220 | Saboteur | 0.865 | yes |
68 | 1998 | 3 | Samurai | 0.860 | no |
69 | 2013 | 148290 | Longhorn | 0.857 | yes |
70 | 2010 | 73439 | Troyes | 0.856 | no |
71 | 2009 | 45134 | Arcana | 0.853 | yes |
72 | 2004 | 9509 | Iglu Iglu | 0.852 | yes |
73 | 2010 | 68182 | Isla Dorada | 0.852 | yes |
74 | 1995 | 915 | Mystery of the Abbey | 0.850 | yes |
75 | 2017 | 200847 | Secrets | 0.847 | no |
76 | 2003 | 8129 | Sluff Off! | 0.845 | yes |
77 | 2008 | 34635 | Stone Age | 0.843 | no |
78 | 2017 | 232043 | Queendomino | 0.842 | yes |
79 | 2013 | 124052 | Cinque Terre | 0.834 | no |
80 | 2015 | 161383 | LIE | 0.833 | yes |
81 | 2008 | 37046 | Ghost Stories | 0.829 | yes |
82 | 2018 | 241478 | Kiwara | 0.828 | yes |
83 | 2007 | 28023 | Jamaica | 0.824 | yes |
84 | 2016 | 204583 | Kingdomino | 0.824 | yes |
85 | 1995 | 112 | Condottiere | 0.820 | yes |
86 | 2006 | 21654 | Iliad | 0.818 | yes |
87 | 2011 | 90930 | Witty Pong | 0.816 | no |
88 | 2007 | 28738 | Kamon | 0.814 | yes |
89 | 2011 | 108783 | Dr. Shark | 0.814 | no |
90 | 2013 | 127024 | Room 25 | 0.811 | no |
91 | 2006 | 27117 | Animalia | 0.810 | yes |
92 | 2014 | 154203 | Imperial Settlers | 0.810 | yes |
93 | 2016 | 205637 | Arkham Horror: The Card Game | 0.810 | yes |
94 | 2018 | 233080 | Book of Dragons | 0.807 | no |
95 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.807 | no |
96 | 2016 | 160010 | Conan | 0.806 | yes |
97 | 2018 | 253470 | Greedy Kingdoms | 0.805 | no |
98 | 2017 | 195373 | BOO | 0.805 | no |
99 | 2018 | 247236 | Duelosaur Island | 0.804 | no |
100 | 2018 | 260428 | Pandemic: Fall of Rome | 0.804 | yes |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.84 |
Decision Tree | roc_auc | binary | 0.77 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think ZeeGarcia is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2012 | 129904 | Shadows over Camelot: The Card Game | 0.954 | no |
2019 | 286096 | Tapestry | 0.951 | no |
2017 | 221107 | Pandemic Legacy: Season 2 | 0.924 | no |
2018 | 259829 | Loser | 0.924 | no |
2005 | 18588 | Les Fils de Samarande | 0.904 | no |
What games does the model think ZeeGarcia is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2002 | 11873 | AMC Reel Clues | 0.003 | yes |
2006 | 25758 | Trüffel-Schnüffel | 0.003 | yes |
2007 | 28843 | 300: The Board Game | 0.003 | yes |
2003 | 6339 | Dying Lights | 0.006 | yes |
2012 | 119506 | Freedom: The Underground Railroad | 0.007 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Noah | The Little Prince: Make Me a Planet | Five Tribes | Mission: Red Planet (Second Edition) | Citadels | Yamataï | Micropolis | Queenz: To Bee or Not to Bee |
2 | Shadows over Camelot: The Card Game | SOS Titanic | Abyss | 7 Wonders Duel | Kanagawa | DIG | Loser | Tapestry |
3 | Okiya | Longhorn | Madame Ching | Raptor | A Game of Thrones: Hand of the King | Pandemic Legacy: Season 2 | Everdell | Kingdomino Duel |
4 | Sky Tango | Cinque Terre | Splendor | LIE | Dice Stars | RUM | Scarabya | Alhambra: Mega Box |
5 | Android: Netrunner | Room 25 | Haru Ichiban | Mysterium | Histrio | Secrets | Kiwara | Tiny Towns |
6 | Button Up! | Pentos | Roll Through the Ages: The Iron Age | Sylvion | Zany Penguins | Queendomino | Book of Dragons | Last Bastion |
7 | Love Letter | Le Fantôme de l'Opéra | Desperados of Dice Town | SHH | Pocket Madness | BOO | Greedy Kingdoms | Era: Medieval Age |
8 | Divinare | Cappuccino | Roll for the Galaxy | GEM | Kingdomino | ORC | Pandemic: Fall of Rome | Ice Team |
9 | Zooloretto: The Dice Game | Sheepzzz | Imperial Settlers | HUE | Arkham Horror: The Card Game | Miaui | Duelosaur Island | Naga Raja |
10 | Descent: Journeys in the Dark (Second Edition) | Asante | Onirim (Second Edition) | Arboretum | Conan | SOW | Jurassic Snack | Conspiracy: Abyss Universe |
11 | Tokaido | Glass Road | Pandemic: The Cure | 504 | Crazy Mistigri | Legend of the Five Rings: The Card Game | Arkham Horror (Third Edition) | Ishtar: Gardens of Babylon |
12 | Agricola: All Creatures Big and Small | Mascarade | Nations: The Dice Game | Between Two Cities | HMS Dolores | Majesty: For the Realm | New Frontiers | The Magnificent |
13 | Escape: The Curse of the Temple | Legacy: The Testament of Duke de Crecy | Akrotiri | Pandemic Legacy: Season 1 | Bloodborne: The Card Game | Ex Libris | KeyForge: Call of the Archons | Marvel Champions: The Card Game |
14 | Think Again! | Gravwell: Escape from the 9th Dimension | Patchwork | Elysium | Pandemic: Reign of Cthulhu | Oliver Twist | Imaginarium | KeyForge: Age of Ascension |
15 | The Hobbit Card Game | Patronize | Linko! | Mondo: Der rasante Legespaß | Hit Z Road | BOX | Railroad Ink: Deep Blue Edition | Herbaceous Sprouts |
16 | Ginkgopolis | BANG! The Dice Game | Dragon Run | Tong | Star Wars: Destiny | Jump Drive | Underwater Cities | Three-Dragon Ante: Legendary Edition |
17 | Africana | Francis Drake | DungeonQuest Revised Edition | Plums | Archaeology: The New Expedition | Merlin | Hokkaido | Machi Koro Legacy |
18 | Targi | Terror in Meeple City | Roll Through the Ages: The Iron Age with Mediterranean Expansion | BUS | Covert | WOO | Dragons | Coralia |
19 | Urbion | The Ravens of Thri Sahashri | Chimera | Blood Rage | Honshū | Azul | Rebel Nox | Amul |
20 | Zombicide | Tash-Kalar: Arena of Legends | Han | Star Trek: Five-Year Mission | Love Letter Premium | The Castles of Burgundy: The Dice Game | Lords of Hellas | Sierra West |
21 | Ohne Furcht und Adel | Forbidden Desert | Camel Up | The Builders: Antiquity | Explorers of the North Sea | Pandemic: Rising Tide | Heroes of Terrinoth | Gates of Delirium |
22 | Il Vecchio | Scotland Yard Master | Pandemic: Contagion | OctoDice | Heir to the Pharaoh | Sagrada | End of the Trail | Yukon Airways |
23 | Thunderstone Advance: Towers of Ruin | Bruges | Fields of Arle | Warehouse 51 | Thunder & Lightning | Nut | Fist of Dragonstones: The Tavern Edition | Run Fight or Die: Reloaded |
24 | Cockroach Poker Royal | BodgerMania | Age of War | Barony | Smash Up: Cease and Desist | King's Life | Azul: Stained Glass of Sintra | Pandemic: Rapid Response |
25 | Android: Infiltration | The Builders: Middle Ages | Till Dawn | SteamRollers | Inis | The Fox in the Forest | Wizardz Bluff | Trails of Tucana |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
ZeeGarcia | owned | validation | GLM | roc_auc | 0.749 |
ZeeGarcia | owned | validation | Decision Tree | roc_auc | 0.703 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.950 | yes |
2020 | 323262 | Velonimo | 0.940 | yes |
2020 | 297661 | Gold River | 0.932 | yes |
2020 | 229782 | Roland Wright: The Dice Game | 0.925 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.877 | yes |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.858 | yes |
2020 | 292917 | Mosquito Show | 0.837 | no |
2020 | 297666 | Jurassic Brunch | 0.794 | yes |
2020 | 303672 | Trek 12: Himalaya | 0.741 | yes |
2020 | 288169 | The Fox in the Forest Duet | 0.708 | no |
2020 | 283155 | Calico | 0.636 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.626 | yes |
2020 | 301880 | Raiders of Scythia | 0.603 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.602 | no |
2020 | 300010 | Dragomino | 0.576 | no |
2020 | 293678 | Stellar | 0.546 | no |
2020 | 293556 | Gloomy Graves | 0.544 | no |
2020 | 270109 | Iwari | 0.531 | yes |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.515 | yes |
2020 | 245659 | Vampire: The Masquerade – Vendetta | 0.498 | no |
2020 | 262208 | Dungeon Drop | 0.487 | no |
2020 | 295486 | My City | 0.461 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.453 | no |
2020 | 267009 | Rome & Roll | 0.445 | no |
2020 | 325635 | Unmatched: Little Red Riding Hood vs. Beowulf | 0.445 | no |
2020 | 294484 | Unmatched: Cobble & Fog | 0.445 | no |
2020 | 318983 | Faiyum | 0.444 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.442 | no |
2020 | 298371 | Wild Space | 0.427 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.422 | yes |
2020 | 293309 | Kraken Attack! | 0.419 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.417 | no |
2020 | 301399 | Lyttle Wood | 0.416 | yes |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.415 | no |
2020 | 303054 | Yacht Rock | 0.407 | no |
2020 | 300531 | Paleo | 0.407 | yes |
2020 | 296667 | Vintage | 0.403 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.393 | no |
2020 | 313698 | Monster Expedition | 0.392 | no |
2020 | 307844 | Atheneum: Mystic Library | 0.392 | no |
2020 | 299607 | Capital Lux 2: Generations | 0.387 | no |
2020 | 302310 | Nanaki | 0.385 | no |
2020 | 296512 | The Game: Quick & Easy | 0.376 | yes |
2020 | 294232 | Stolen Paintings | 0.376 | no |
2020 | 293014 | Nidavellir | 0.373 | no |
2020 | 284777 | Unmatched: Jurassic Park – InGen vs Raptors | 0.370 | no |
2020 | 299592 | Beez | 0.364 | no |
2020 | 324345 | キャットインザボックス (Cat in the box) | 0.362 | no |
2020 | 311031 | Five Three Five | 0.361 | no |
2020 | 318183 | Prehistories | 0.357 | yes |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2021 | 332944 | Sobek: 2 Players | 0.952 | yes |
2 | 2021 | 340041 | Kingdomino Origins | 0.840 | yes |
3 | 2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.809 | no |
4 | 2021 | 329670 | Pandemic: Hot Zone – Europe | 0.807 | no |
5 | 2021 | 334644 | Nicodemus | 0.710 | yes |
6 | 2021 | 344415 | Trek 12: Amazonia | 0.687 | no |
7 | 2021 | 303676 | Oh My Brain | 0.680 | yes |
8 | 2022 | 295374 | Long Shot: The Dice Game | 0.673 | yes |
9 | 2021 | 329714 | Dreadful Circus | 0.673 | no |
10 | 2021 | 290236 | Canvas | 0.649 | no |
11 | 2021 | 340466 | Unfathomable | 0.618 | no |
12 | 2021 | 314491 | Meadow | 0.610 | no |
13 | 2021 | 328535 | Mandragora | 0.609 | yes |
14 | 2021 | 331635 | Kameloot | 0.583 | no |
15 | 2021 | 340237 | Wonder Book | 0.560 | no |
16 | 2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.557 | no |
17 | 2021 | 285967 | Ankh: Gods of Egypt | 0.549 | yes |
18 | 2022 | 353470 | Star Wars: Jabba's Palace – A Love Letter Game | 0.537 | no |
19 | 2022 | 315610 | Massive Darkness 2: Hellscape | 0.522 | no |
20 | 2021 | 339906 | The Hunger | 0.522 | no |
21 | 2021 | 341358 | INSERT | 0.516 | no |
22 | 2021 | 340677 | Bad Company | 0.508 | no |
23 | 2021 | 304783 | Hadrian's Wall | 0.507 | no |
24 | 2021 | 339789 | Welcome to the Moon | 0.490 | no |
25 | 2022 | 332393 | Bridge City Poker | 0.486 | no |
26 | 2022 | 338364 | Pumafiosi | 0.484 | no |
27 | 2021 | 315937 | X-Men: Mutant Insurrection | 0.477 | no |
28 | 2021 | 344408 | Full Throttle! | 0.475 | no |
29 | 2023 | 349793 | Age of Rome | 0.461 | no |
30 | 2021 | 316080 | KeyForge: Dark Tidings | 0.453 | no |
31 | 2021 | 356907 | Mascarade (second edition) | 0.452 | no |
32 | 2021 | 324242 | Sheepy Time | 0.450 | no |
33 | 2021 | 340834 | Gravwell: 2nd Edition | 0.450 | no |
34 | 2021 | 329465 | Red Rising | 0.440 | no |
35 | 2021 | 333553 | For the King (and Me) | 0.434 | no |
36 | 2021 | 329529 | Magellan: Elcano | 0.431 | no |
37 | 2022 | 356996 | The Border | 0.402 | no |
38 | 2022 | 351476 | My City: Roll & Write | 0.392 | no |
39 | 2021 | 339905 | Love Letter: Princess Princess Ever After | 0.390 | no |
40 | 2021 | 329084 | Space Dragons | 0.385 | no |
41 | 2022 | 356033 | Libertalia: Winds of Galecrest | 0.382 | no |
42 | 2021 | 286751 | Zombicide: 2nd Edition | 0.377 | no |
43 | 2022 | 275215 | Namiji | 0.372 | no |
44 | 2021 | 322014 | All-Star Draft | 0.372 | no |
45 | 2021 | 324856 | The Crew: Mission Deep Sea | 0.372 | no |
46 | 2021 | 344258 | That Time You Killed Me | 0.371 | no |
47 | 2022 | 308028 | Drop Drive | 0.370 | no |
48 | 2021 | 310198 | Escape: Roll & Write | 0.365 | no |
49 | 2021 | 336382 | Marvel United: X-Men | 0.363 | no |
50 | 2021 | 295947 | Cascadia | 0.362 | yes |
51 | 2021 | 316343 | Capital Lux 2: Pocket | 0.362 | no |
52 | 2021 | 346703 | 7 Wonders: Architects | 0.361 | no |
53 | 2021 | 320069 | Tavern Tales: Legends of Dungeon Drop | 0.345 | no |
54 | 2021 | 322339 | Hanamikoji: Geisha's Road | 0.343 | no |
55 | 2021 | 335541 | We Care: a Grizzled Game | 0.342 | no |
56 | 2021 | 313262 | Shamans | 0.339 | no |
57 | 2021 | 331549 | MiniQuest Adventures | 0.338 | no |
58 | 2022 | 338460 | The Isle of Cats: Explore & Draw | 0.335 | no |
59 | 2022 | 341945 | La Granja: Deluxe Master Set | 0.326 | no |
60 | 2022 | 335764 | Unmatched: Battle of Legends, Volume Two | 0.326 | no |
61 | 2021 | 339790 | Cocktail | 0.324 | no |
62 | 2022 | 275284 | Arkeis | 0.322 | no |
63 | 2021 | 346553 | Heuschrecken Poker | 0.316 | no |
64 | 2021 | 286667 | Tutankhamun | 0.311 | no |
65 | 2021 | 322282 | Momiji | 0.301 | no |
66 | 2021 | 333055 | Subastral | 0.299 | no |
67 | 2021 | 335678 | Let's Make a Bus Route: The Dice Game | 0.299 | yes |
68 | 2021 | 316287 | Quest | 0.292 | no |
69 | 2022 | 353765 | Awimbawé | 0.289 | no |
70 | 2021 | 341048 | Free Ride | 0.289 | no |
71 | 2021 | 300523 | Biblios: Quill and Parchment | 0.286 | no |
72 | 2021 | 330608 | Cryo | 0.286 | no |
73 | 2021 | 344405 | Cartaventura: Oklahoma | 0.283 | no |
74 | 2022 | 350316 | Wayfarers of the South Tigris | 0.283 | no |
75 | 2021 | 300305 | Nanga Parbat | 0.281 | no |
76 | 2022 | 319910 | Pagan: Fate of Roanoke | 0.278 | no |
77 | 2021 | 311990 | Macaron | 0.274 | no |
78 | 2021 | 344839 | Dog Lover | 0.272 | yes |
79 | 2021 | 323156 | Stroganov | 0.272 | no |
80 | 2021 | 337765 | Brian Boru: High King of Ireland | 0.272 | no |
81 | 2021 | 283242 | The Whatnot Cabinet | 0.271 | no |
82 | 2021 | 345036 | Qwixx Longo | 0.268 | no |
83 | 2022 | 281258 | Sub Terra II: Inferno's Edge | 0.267 | no |
84 | 2022 | 324894 | Free Radicals | 0.266 | no |
85 | 2022 | 310873 | Carnegie | 0.264 | no |
86 | 2021 | 331946 | Faux Diamonds | 0.263 | no |
87 | 2021 | 322560 | Maeshowe: an Orkney Saga | 0.262 | no |
88 | 2022 | 288080 | Dice Realms | 0.261 | no |
89 | 2021 | 275557 | The Last Bottle of Rum | 0.261 | no |
90 | 2021 | 295607 | Canopy | 0.258 | yes |
91 | 2021 | 257706 | Zoo-ography | 0.258 | no |
92 | 2021 | 304324 | Dive | 0.255 | no |
93 | 2021 | 282776 | Tumble Town | 0.252 | no |
94 | 2021 | 342073 | Berried Treasure | 0.252 | no |
95 | 2021 | 341362 | Reapers | 0.249 | no |
96 | 2021 | 281248 | Cape May | 0.248 | no |
97 | 2021 | 327076 | Cartaventura: Lhasa | 0.247 | no |
98 | 2021 | 327077 | Cartaventura: Vinland | 0.247 | no |
99 | 2022 | 340672 | Council of 12 | 0.246 | no |
100 | 2022 | 347702 | Las Vegan | 0.245 | no |